Accidental falls and slips are significant contributors to injuries and, potentially, death, among the elderly and the sick. In many cases, falls can result in broken bones, head injuries or other injuries such as sprains or ligament tears. Annually, 2.5 million Americans are treated in an emergency room for fall-related injuries and up to 700,000 Americans are hospitalized each year for serious injuries stemming from head injuries or hip fractures. Adjusted for inflation, direct medical costs arising from injuries from a fall result in nearly $3.4 billion dollars annually, most of which are hospital costs. While prior art fall detection systems exist, the prior art systems often are subject to several disadvantages. Some prior art systems include a fall detection system on a wearable device or fall monitor device that run continuously in the foreground of wearable device or fall monitor device processor. These prior art systems are capable of continuously detecting fall events in substantially real time. However, in these prior art systems, wearable devices or fall monitoring devices are always “awake” and devote substantial processing resources to the fall detection system. This is a burden on the battery life of wearable devices or fall monitoring devices, and increase downtime due to frequent recharges, thereby reducing the effectiveness of the monitoring system. Other prior art systems rely on accelerometer data from mobile phones or smart phones, typically kept in a pocket or on a belt holster, to detect fall events or fall injuries. However, these solutions are also imperfect. Mobile phones aren't always kept on the body at all times, thus limiting the time during which fall detection and monitoring can be active.